MétaCan
Menu
Back to cohort

Songs for the Dead

2022· article· en· W4319591279 on OpenAlexaffabout
Alan McFetridge, Antoinette Johnson, Emma Mcloughin, Dan Devitt

Bibliographic record

VenueSophia · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPhotographySubject (documents)HistorySociologyVisual artsArt

Abstract

fetched live from OpenAlex

The Last Man - photo by Alan McFetridge Songs of the Dead is a photographic exploration of the aftermath of a devastating fire that impacted the community of Fort McMurray in Alberta Canada on the 3rd of May 2016. Six months after the fire, stimulated by media coverage and reflecting on the discourse surrounding dispossession and the environment, the project commenced at ground level with support of a Royal Photographic Society Environmental Awareness Bursary in a region inhabited by Anishinaabe1 located within Treaty 8 Territory, the traditional lands of the Cree, Dene and unceded territory of the Métis. The visual essay presented here is centred in the wake of a major fire event, however, it is also about human law and ecosystems. By traversing discussions on ethics within documentary photography and briefly exploring the history Aftermath of this medium, we argue how photography can better address socio-ecological issues within climate change through poetics. We offer a way of resisting the norms of documentary photography, resulting from subject and process-driven methods.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.066
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0070.004
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0350.006

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.351
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueSophiaSame topicGeographies of human-animal interactionsFrench-language works237,207